You open the folder Monday morning and the count is already at fourteen. Fourteen pieces the model generated between Friday night and now—each one clean, each one needing the same two sets of eyes that have always carried the weight. One of those eyes belongs to the guy who still teaches the 7 a.m. Bible study; the other belongs to you.
Nobody on the team feels faster. The calendar just feels heavier.
One pilot involved sermon prep for a multi-site church. Pastors generated opening illustrations in bulk. Review time tripled because each illustration required a quick check for doctrinal alignment and a second check for stories already used in the same series. One pastor noted the verification step now consumed the slot previously reserved for prayer and reflection before final edits.
Another pilot targeted volunteer onboarding packets. Generation of welcome scripts moved from days to hours. The volunteer coordinator then spent an unexpected afternoon reconciling the new scripts with the existing print-first formatting rules that had been refined over three years. The time saved on creation reappeared as formatting corrections and one extra round of printer proofs.
Each case followed the same pattern: the generation step shrank while the verification and integration steps expanded at roughly the same ratio.
Latticework view of where generation meets sign-off
Charlie Munger’s latticework approach requires holding several mental models at once rather than optimizing a single variable. Applied here, the models include queueing theory, incentive misalignment, and the difference between local and system throughput. Faster generation improves the local metric of draft completion. It degrades the system metric of finished, approved assets ready for volunteers.
Queueing theory shows that adding work to a constrained reviewer creates backlog faster than linear models predict. Incentive misalignment appears when the person praised for producing more drafts is not the same person who absorbs the review cost. The local gain registers; the system cost stays hidden until retention or burnout metrics move.
Munger would also note the second-order effect on judgment quality. When reviewers face stacked queues, they default to surface checks rather than the deeper cross-references that previously caught subtle issues. The latticework therefore points to redesigning the handoff itself, not celebrating the generation speed.
Metrics that track the full loop instead of just generation
Most dashboards still report “drafts produced” or “tokens generated.” These numbers rise while the date an asset reaches the volunteer inbox stays flat or slips. Replace or supplement them with cycle time from prompt to approved asset and with reviewer hours per approved asset. Both numbers expose the actual constraint.
Add a third metric: percentage of generated content that survives first review without revision. Low survival rates indicate the generation step is producing volume that still requires heavy human correction. Track this weekly on one workflow before expanding.
Finally, measure downstream volunteer completion rate on the delivered material. If faster production leads to lower volunteer follow-through because the content feels less tailored, the speed gain has created a new problem rather than solving an old one.
Your Turn: Apply This Today
- Pick one AI-assisted workflow that currently ends at “draft generated” and add a timestamp field for the moment the first human reviewer opens the file.
- Log the exact minutes spent on theological alignment, formatting, and final sign-off for the next ten items that move through that workflow.
- Calculate the ratio of generation time to total review time and post the number where the team can see it each Friday.
- Identify the single reviewer who absorbs the largest share of verification work and block two hours on their calendar next week labeled “no new drafts.”
- Change the success definition in the next sprint from “ten new drafts created” to “six assets approved and formatted for print.”
- Run the same workflow for one more week while recording the survival rate of first drafts after initial review, then adjust the prompt template based on the patterns that fail most often.
Two earlier posts on this blog examined similar constraints: The Context Window That Only Closed When a Real Coordinator Sat Down and The Burnout Number Managers Still Ignore Until Retention Breaks.
I consult with ministry product leaders on AI workflow instrumentation, verification loops, and adoption metrics that reflect full human effort. Let’s talk.